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Capacity and Flow Management in Healthcare Delivery Systems with Multi-priority Patients

Torabi, Elham
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470043863

Abstract Details

2016, PhD, University of Cincinnati, Business: Business Administration.
In healthcare services, the goal is to provide timely and high-quality care. However, given the high costs of healthcare, resource shortages, and increasing demand, efficiency is also crucial. High variability, together with cost pressures, make matching capacity and demand challenging for healthcare systems. Despite the need for being maximally efficient, health centers need to be responsive to patients as well, especially if there are patients who need emergent (immediate) care. Examples are operating rooms, with both elective (low-priority) and emergency (high-priority) patients, and emergency departments in which patients are prioritized into five classes based on medical urgency and resource requirements. The focus is to match supply and demand to ensure smooth patient flow in the system. This requires careful capacity and flow decisions, which involves balancing the tradeoff between efficiency and responsiveness in the presence of multiple priority classes of patients. Thus, we study two major operational decisions that arise when serving multi-priority streams of patients: 1) resource allocation, and 2) flow allocation. These decisions are interrelated and have a direct effect on system performance measured by many different metrics, such as patient wait time, patient flow time, and throughput. We start from a two-priority system motivated by operating room systems with emergency and non-emergency patients (Chapter 2). Then we explore the more complicated case of multi-priority systems motivated by emergency departments with several priority classes of patients (Chapters 3 and 4). For measuring the performance of different policies, we use patient waiting time as a proxy for system responsiveness. Using simulation and optimization methods, we identify capacity and flow allocation policies that minimize wait time, thus maximizing system responsiveness. We capitalize on statistical methods and data-mining techniques to help inform the operational and theoretical models, including those based on queueing theory, optimization, and simulation. The results indicate that in systems with multiple priority classes of patients, both aggregating (pooling) and disaggregating capacity can be useful and helpful, depending on the patient populations involved and the system’s larger operational objectives. Our investigations highlight the importance of flow allocation decisions and the impact they can have on system responsiveness.
Craig Froehle, Ph.D. (Committee Chair)
Michael Magazine, Ph.D. (Committee Member)
Uday Rao, Ph.D. (Committee Member)
Denise White, Ph.D. (Committee Member)
115 p.
Operations Research
Operations Management; Healthcare Operations; Multi-priority Queues; Optimization; Patient Triage

Recommended Citations

Citations

  • Torabi, E. (2016). Capacity and Flow Management in Healthcare Delivery Systems with Multi-priority Patients [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470043863

    APA Style (7th edition)

  • Torabi, Elham. Capacity and Flow Management in Healthcare Delivery Systems with Multi-priority Patients. 2016. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470043863.

    MLA Style (8th edition)

  • Torabi, Elham. "Capacity and Flow Management in Healthcare Delivery Systems with Multi-priority Patients." Doctoral dissertation, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470043863

    Chicago Manual of Style (17th edition)

ucin1470043863
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© 2016, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.